There are a couple popular deep learning based ReID frameworks such as torchreid and fastreid. There projects are very helpful for benchmarking SOTA methods as well as implementing new ideas quickly. For my personal projects, I've been heavily using these projects. The one problem that reduced my productivity was how I needed to add configuration defaults for every module that I added. Inspired by OpenMMLab's projects, I created my own modular framework that uses mmcv that significantly reduced this complexity.

Key points:

• Customisable: experiments can be configured easily with the help of mmcv; no more bloated configs! pepper modules can be integrated into other projects such as mmcls and mmdet.
• Scalable: add modules easily with "registries" and implement new ideas quickly without the hassle of breaking things
• Fast: training and evaluations are done using distributed processing
• Robust: borrows and implements techniques from other projects

Other features:

• supports image and video ReID
• supports various datasets (including MOT datasets)
• supports cross-dataset evaluation
• supports training on multiple datasets
• multi-process multi-gpu distributed training
• separate dataset preparation scripts
• etc...

Notes:

• I will get around to creating a detailed documentation later, but for now please read the code or reference similar frameworks such as mmcls and mmdet.
• Please open issues or PR if you spot any bugs or improvements.

Clone the project:

git clone --recursive [email protected]:haruishi43/pepper.git
cd pepper

• torch and torchvision
• mmcv
• faiss-gpu

Other dependencies can be installed using the following command:

pip install -r requirements.txt

Two options:

1. Install pepper as a global library:

python setup.py develop
# or
pip install -e .

2. Install locally:

No need to run any commands except for when you want the optimized evaluation functionality:

cd pepper/core/evaluation/rank_cylib; make all

CUDA_VISIBLE_DEVICES=<gpu_ids> ./tools/dist_train.sh <config> <num_gpus>

• Bag-of-Tricks
• Video-Person-ReID
• Metric Learning on MNIST

• README
• Documentation
• Upload model weights
• Test codes
• PyPI installation
• Update to 1.0